The DOI matches its Crossref record; 29 of the 29 references it cites were found in a registry or an index.
Modeling Actions through State Changes
Abstract
In this paper we present a model of action based on the change in the state of the environment. Many actions involve similar dynamics and hand-object relationships, but differ in their purpose and meaning. The key to differentiating these actions is the ability to identify how they change the state of objects and materials in the environment. We propose a weakly supervised method for learning the object and material states that are necessary for recognizing daily actions. Once these state detectors are learned, we can apply them to input videos and pool their outputs to detect actions. We further demonstrate that our method can be used to segment discrete actions from a continuous video of an activity. Our results outperform state-of-the-art action recognition and activity segmentation results.
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Citations
Cites
Distinctive Image Features from Scale-Invariant Keypoints
Lowe, D. G. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000029664.99615.94 · Cited by 55,589
Histograms of Oriented Gradients for Human Detection
Dalal, N., & Triggs, B. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.177 · Cited by 33,584
Artificial intelligence: A modern approach
Nilsson, N. J. (1996) · Artificial Intelligence · doi:10.1016/0004-3702(96)00007-0 · Cited by 10,730
Learning realistic human actions from movies
Ivan Laptev, A., Marszałek, M., Schmid, C., & Rozenfeld, B. (2008) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2008.4587756 · Cited by 3,481
Recognizing human actions: a local SVM approach
Schüldt, C., Laptev, I., & Barbara Caputo, B. (2004) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2004.1334462 · Cited by 3,248
The recognition of human movement using temporal templates
Bobick, A., & Davis, J. W. (2001) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.910878 · Cited by 2,837
A survey of advances in vision-based human motion capture and analysis
Moeslund, T. B., Hilton, A., & Krüger, V. (2006) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2006.08.002 · Cited by 2,598
Machine Recognition of Human Activities: A Survey
Turaga, P. K., Chellappa, R., Subrahmanian, V. S., & Udrea, O. (2008) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2008.2005594 · Cited by 1,357
Recognizing action at a distance
Efros, A. A., Berg, Mori, & Malik (2003) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2003.1238420 · Cited by 1,242
Marszałek, M., Laptev, I., & Schmid, C. (2009) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2009.5206557 · Cited by 1,197
Cited by
Sorted by citations
Temporal Convolutional Networks for Action Segmentation and Detection
Lea, C., Flynn, M. D., Vidal, R., Reiter, A., & Hager, G. D. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.113 · Cited by 2,238
“In the literature, our task goes by the name action segmentation [7, 8, 6, 15, 29, 16, 10] or action detection [28, 20, 21, 25].”
MethodMS-TCN: Multi-Stage Temporal Convolutional Network for Action Segmentation
Farha, Y. A., & Gall, J. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00369 · Cited by 673
“While traditional approaches use a sliding window approach with non-maximum suppression [22, 11], Fathi and Rehg [7] model actions based on the change in the state of objects and materials.”
MethodEgo4D: Around the World in 3,000 Hours of Egocentric Video
Grauman, K., Westbury, A., Byrne, E., Chavis, Z., Furnari, A., Girdhar, R., Hamburger, J., Jiang, H., Liu, M., Liu, X., et al. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01842 · Cited by 630
Self-citationA Survey on Activity Detection and Classification Using Wearable Sensors
Cornacchia, M., Ozcan, K., Zheng, Y., & Velipasalar, S. (2016) · IEEE Sensors Journal · doi:10.1109/jsen.2016.2628346 · Cited by 427
MS-TCN++: Multi-Stage Temporal Convolutional Network for Action Segmentation
Li, S., Farha, Y. A., Liu, Y., Cheng, M.-M., & Gall, J. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.3021756 · Cited by 349
“Other approaches model actions based on the change in the state of objects and materials [18] or based on the interactions between hands and objects [19].”
Background
Metadata
- Authors
- Fathi, A., & Rehg, J. M.
- Affiliations
- Georgia Institute of Technology (US)
- Venue
- 2013 IEEE Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2013-06
- Pages
- 2579-2586
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W1967686239
- Cited by
- 145
- References
- 29
- Citations by year
- 2026: 4 · 2025: 7 · 2024: 11 · 2023: 9 · 2022: 10 · 2021: 12 · 2020: 11 · 2019: 12 · 2018: 11 · 2017: 12 · 2016: 23 · 2015: 18 · 2014: 5
- FWCI
- 9.78
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- SMARTech Repository (Georgia Institute of Technology) (submittedVersion)
- Funders
- National Science Foundation
- Multidisciplinary University Research Initiative
- Army Research Office
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Human Pose and Action Recognition
- Physical Sciences › Computer Science › Human-Computer Interaction › Hand Gesture Recognition Systems
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Keywords
- human action recognition, weakly supervised learning, activity segmentation, hand-object interaction, video action detection
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